Neurobiology Unit 4

Neurobiology Unit 4

• Unit 4 covers the basics of neurons, glial cells, and brain structures.
• It examines artificial neural networks and their differences from biological neural networks, along with application areas like machine learning and data mining in biology.

Nervous System

• The nervous system coordinates mental processes like perception, action, learning, and memory.
• The human brain is a network of interconnected nerve cells (neurons) and glial cells (glia).

Supporting Cells (Neuroglial Cells)

• Neuroglia (in the CNS) supports cells in the central nervous system (CNS) and can refer to cells of the peripheral nervous system (PNS).
• Glial cells outnumber neurons by a ratio of 10 to 1.
• They comprise half the brain's mass.
• Glial cells can divide throughout life.
• They have branching processes and a central cell body.

Glial Cells

• Glial cells are support cells.
• There are significantly more glial cells than neurons in the human brain (between 10 and 50 times more).
• Glial cells surround neurons but do not connect them.
• Their name derives from the Greek word for glue.

Types of Glial Cells

• Astrocytes: The most abundant glial type, having irregular star-shaped bodies. They take up and release ions to control the environment around neurons.
• Astrocytes also play a role in synapse formation during development and recycle neurotransmitters. They produce molecules necessary for neural growth and propagate calcium signals that may be involved in memory.
• Oligodendrocytes: Possess few branches, primarily in the CNS, and wrap their cell processes around axons to produce myelin sheaths. Myelin sheaths aid in rapid nerve impulse conduction.
• Schwann cells: Surrounding axons in the PNS, forming myelin sheaths around axons of the PNS.
• Microglia: The smallest and least abundant glial cell type. They serve as the macrophages of the CNS, engulfing invading microorganisms and dead neurons. Microglia originate from blood cells called monocytes.

Nerve Cells - Neurons

• Nerve cells are the main signaling units of the nervous system.
• A typical neuron comprises: the cell body, dendrites, axon, and presynaptic terminal.
• The cell body (soma) acts as the metabolic center, containing the nucleus (with genes) and endoplasmic reticulum for protein synthesis.
• Dendrites are short, branching processes extending out from the cell body which receive incoming signals from other neurons.
• The axon is a long, tubular process that conducts signals away from the cell body to other neurons (or effectors like muscles).

Action Potential

• A neuron receives input from other neurons (typically thousands of signals).
• These signals are integrated.
• Once the input exceeds a critical level, a spike (electrical pulse) discharges down the axon.
• This is followed by a refractory period, where the neuron cannot fire.

Synapse

• The axon endings (output zone) come very close to the dendrites or cell body of the next neuron.
• Transmission across the synapse (gap) involves neurotransmitters - chemicals released by the first neuron; binding to receptors in the second neuron.
• The amount of neurotransmitter, receptor availability, and reabsorption influence signal transmission across the synapse.

Sensory Input and Motor Output

• Sensory signals are picked up by sensory receptors and carried by afferent nerve fibers of the PNS to the CNS.
• Motor signals are carried away from the CNS through efferent nerve fibers of the PNS to effectors such as muscles and glands.

Neural Networks in the Brain

• The brain is not homogeneous but comprises distinct regions (cortex, midbrain, brainstem, cerebellum), with hierarchical subdivisions.
• The brain has a dense network of local connections between neurons.
• These connections, along with long-range ones, form very dense, complex local networks.

Computer-based Neural Networks

• Neural networks use parallel processing, unlike conventional serial computers.
• Neural networks, while built with slow hardware, have remarkable capabilities in handling partial damage and adapting from experience.
• These networks are used for complex visual perception, intelligence, and self-awareness.

ANN Applications

• ANNs find widespread applications in aerospace, automotive, banking, cognitive science, defense, electronics, entertainment, financial, and insurance sectors. Also in Manufacturing and medical fields.

Data Mining in Biology

• Data mining in biology refers to extracting useful information from large biological datasets to understand patterns and trends.
• The primary goals of data mining in biology include prediction and description.
• Data mining finds applications in various biological domains, including diabetes.

Machine Learning

• Machine learning is a field focused on giving computers the ability to learn without explicit programming.
• Machine learning is closely related to statistics, utilizing techniques like regression, classification, and clustering.

Types of Machine Learning

• Supervised Learning: Requires external assistance, often from experts or humans, guiding the algorithm by providing labeled training data and the associated desired outputs, for producing a model.
• Unsupervised Learning: Does not require external intervention. Algorithms work on unlabeled data, inferring patterns and grouping data into clusters based on inherent properties.
• Reinforcement Learning: Algorithms learn from interactions with an environment by trying different actions, receiving rewards or penalties (feedback), and adjusting behavior based on that feedback to optimize performance.

Deep Learning

• Deep learning is a more recent subfield of machine learning that utilizes multiple layers of processing elements mimicking human brain function.
• This layered structure allows algorithms to handle high-dimensional data more effectively.
• Deep learning is similar to neural networks but with multi-layered structures. These multi-layered nodes try to mimic how the human brain thinks to solve complex problems.

Applications of ML in Biology\

• ML aids in selecting crucial features from high-dimensional biological datasets.
• Significant use of ML in classifying and clustering biological data (like gene data) is shown.

Disorders of the Nervous System

• Multiple sclerosis is a common cause of neural disability, often varying in intensity among affected individuals.
• The cause of multiple sclerosis remains incompletely understood.
• This is considered an autoimmune disease where the immune system attacks the myelin around axons in the CNS.

Case Study: Data Mining in Diabetes

• Diabetes is a prevalent endocrine disease.
• Regression analysis was used to predict effects of different treatments on patients of varying age groups (young vs. old).

Alzheimer's Disease and Parkinson's

• Alzheimer's Disease: Age-associated cognitive decline, characterized by amyloid plaques and neurofibrillary tangles which interfere with neuronal functions leading to lost cholinergic functions.
• Parkinson's Disease: Age-associated movement disorder, marked by rigidity, incoordination, and loss of dopaminergic neuronal function.

Other Neurodegenerative Diseases

• A list of diseases involving progressive neuronal damage and loss. Includes diseases like Alexander's disease, Amyotrophic Lateral Sclerosis, Multiple Sclerosis, and others.

This quiz focuses on the fundamentals of neurons, glial cells, and the structures of the brain. It also explores the distinctions between artificial and biological neural networks, along with their applications in machine learning and biology. Test your knowledge on the nervous system and supportive cells.